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from collections import defaultdict from collections.abc import Iterable from functools import reduce @@ -9,7 +11,7 @@ import numpy as np from numpy.typing import NDArray -from scipy.interpolate import BSpline +from pydantic import model_validator from qibolab._core.components import Config from qibolab._core.components.configs import AcquisitionConfig @@ -25,7 +27,18 @@ from qibolab._core.sequence import PulseSequence from qibolab._core.sweeper import ParallelSweepers -from .engine import Operator, OperatorEvolution, QutipEngine, SimulationEngine +from .engine import ( + Operator, + OperatorEvolution, + QutipEngine, + SimulationEngine, + TimeDependentOperator, +) +from .engine.abstract import ( + HAMILTONIAN_FILENAME, + SIMULATOR_CONFIG, + SWEEP_SIMULATION_FILENAME, +) from .hamiltonians import ( HamiltonianConfig, Modulated, @@ -33,8 +46,6 @@ ) from .results import acquisitions, index, results -SPLINE_INTERP_ORDER = 3 -"""Polynomial order used for interpolating the pulses with a spline function.""" NYQUIST_FREQUENCY = 20 """GHz, Nyquist frequency used for computing the solution and resolve qubit oscillations.""" SAMPLING_INTERVAL = 1 / (2 * NYQUIST_FREQUENCY) @@ -51,16 +62,26 @@ class EmulatorController(Controller): """Sampling rate used during simulation.""" engine: SimulationEngine = QutipEngine() """SimulationEngine. Default is QutipEngine.""" - save_dir: Path | None = None + save_dir: os.PathLike | str | None = None """Flag for saving the full system evolution computed from the simulation backend. In order to set it True modify `platform.py` file in the platform folder.""" + @model_validator(mode="after") + def validate_save_dir(self): + if self.save_dir is not None: + # converting every possible output as a pathlib.Path object + save_dir = Path(self.save_dir) + if save_dir.exists(): + raise FileExistsError("The given data folder already exists.") + object.__setattr__(self, "save_dir", save_dir) + return self + @property def sampling_rate(self) -> float: return self.sampling_rate_ @sampling_rate.setter - def sampling_rate(self, value: float): + def sampling_rate(self, value: float) -> float: self.sampling_rate_ = value def connect(self): @@ -69,22 +90,55 @@ def connect(self): def disconnect(self): """Dummy disconnect method.""" - def _dump_simulation(self, sequence, configs, states, coefficients): + def _dump_simulation( + self, + sequence_idx, + static_ham: Operator, + evolution: OperatorEvolution, + states: NDArray, + simulation_config: dict, + ) -> None: """Write operators (once), time coefficients (n-d), density matrices (n-d).""" - self.save_dir.mkdir(parents=True, exist_ok=True) - - # operators: run-invariant, so build them once here (outside the sweep loop) - config = cast(HamiltonianConfig, configs["hamiltonian"]) - static = config.hamiltonian(config=configs, engine=self.engine) - evolution = self._pulse_hamiltonian(sequence, configs) - time_ops = ( - [pair[0] for pair in evolution.operators] if evolution is not None else [] + + if self.save_dir is None: + return + + sequence_dir = self.save_dir / f"sequence_{sequence_idx}" + sequence_dir.mkdir(parents=True, exist_ok=True) + + # list of file coefficients; NOTE: the first element is always the simulation timesteps array + time_coefficients: list[NDArray] = np.stack( + [evolution.times] + [c for _, c in evolution.operators] ) - operators = [static] + time_ops - self.engine.save_operators(operators, self.save_dir) - np.save(self.save_dir / "time_coefficients.npy", coefficients) - np.save(self.save_dir / "density_matrices.npy", states) + # solver configuration file path + json_filename = sequence_dir / (SIMULATOR_CONFIG + ".json") + static_hamiltonian_filename = sequence_dir / (HAMILTONIAN_FILENAME + ".npy") + + # check if the the sweeper-independent data for the current sequence have already been dumped + if not static_hamiltonian_filename.exists(): + # list of file operators of the pulse sequence; NOTE: the first element is always the time independent hamiltonian + operators = np.stack( + [static_ham.full()] + [op.full() for op, _ in evolution.operators] + ) + np.save(static_hamiltonian_filename, operators) + + if not json_filename.exists(): + with open(json_filename, "w") as f: + json.dump(simulation_config, f) + + # NOTE: this might crash if the process is parallelized, to be review once we enable parallelization + sweep_idx = sum( + 1 + for file in sequence_dir.iterdir() + if file.is_file() and SWEEP_SIMULATION_FILENAME in file.name + ) + np.savez( + sequence_dir / (SWEEP_SIMULATION_FILENAME + f"_{sweep_idx}.npz"), + time_coeffs=time_coefficients, + results=states, + sim_config=simulation_config, + ) def play( self, @@ -105,7 +159,7 @@ def play( results_to_process = ( self._play_sequence(configs, sequence, options, sweepers) - for sequence in sequences_ + for sequence in enumerate(sequences_) ) return reduce(or_, results_to_process) @@ -113,7 +167,7 @@ def play( def _play_sequence( self, configs: dict[str, Config], - sequence: PulseSequence, + sequence: tuple[int, PulseSequence], options: ExecutionParameters, sweepers: list[ParallelSweepers], ): @@ -122,21 +176,19 @@ def _play_sequence( Executes a sweep of the quantum sequence and processes the results into a structured results object containing quantum states and measurement data. """ - sweep_states, sweep_coefficients = self._sweep(sequence, configs, sweepers) - if self.save_dir is not None: - self._dump_simulation(sequence, configs, sweep_states, sweep_coefficients) + sweep_states = self._sweep(sequence, configs, sweepers) hamiltonian = cast(HamiltonianConfig, configs["hamiltonian"]) return results( # states in computational basis states=sweep_states, - sequence=sequence, + sequence=sequence[1], hamiltonian=hamiltonian, options=options, ) def _sweep( self, - sequence: PulseSequence, + sequence: tuple[int, PulseSequence], configs: dict[str, Config], sweepers: list[ParallelSweepers], updates: dict | None = None, @@ -155,7 +207,6 @@ def _sweep( return self._evolve(sequence, configs, updates) state_slices: list[NDArray] = [] - coeff_slices = [] parsweep = sweepers[0] # execute once for each parallel value for values in zip(*(s.values for s in parsweep)): @@ -167,15 +218,16 @@ def _sweep( if sweeper.channels is not None: for channel in sweeper.channels: updates[channel].update({sweeper.parameter.name: value}) - states, coeffs = self._sweep(sequence, configs, sweepers[1:], updates) - state_slices.append(states) - coeff_slices.append(coeffs) + state_slices.append(self._sweep(sequence, configs, sweepers[1:], updates)) # stack all slices in a single array, along the current outermost dimension - return np.stack(state_slices), np.stack(coeff_slices) + return np.stack(state_slices) def _evolve( - self, sequence: PulseSequence, configs: dict[str, Config], updates: dict + self, + sequence_tuple: tuple[int, PulseSequence], + configs: dict[str, Config], + updates: dict, ) -> NDArray: """Evolve a pulse sequence on the quantum emulator. @@ -183,6 +235,8 @@ def _evolve( the time-dependent Hamiltonian, evolves the initial state with optional collapse operators, and returns the resulting measurement data. """ + sequence_identifier, sequence = sequence_tuple + sequence_ = update_sequence(sequence, updates) configs_ = update_configs(configs, updates) config = cast(HamiltonianConfig, configs_["hamiltonian"]) @@ -194,7 +248,7 @@ def _evolve( measurement_times[measurement_times < SAMPLING_INTERVAL] = SAMPLING_INTERVAL tlist_, index = np.unique(measurement_times, return_inverse=True) - results = self.engine.evolve( + results, simulation_configs = self.engine.evolve( hamiltonian=hamiltonian, initial_state=config.initial_state(self.engine), time=np.concatenate(([0], tlist_)), @@ -202,14 +256,19 @@ def _evolve( time_hamiltonian=time_hamiltonian, ) states = np.stack([s.full() for s in results.states[1:]])[index] - coefficients = ( - time_hamiltonian.coefficients if time_hamiltonian is not None else None + + self._dump_simulation( + sequence_identifier, + hamiltonian, + time_hamiltonian, + states, + simulation_configs, ) - return states, coefficients + return states def _pulse_hamiltonian( self, sequence: PulseSequence, configs: dict[str, Config] - ) -> OperatorEvolution | None: + ) -> OperatorEvolution: """Construct Hamiltonian time dependent term for qutip simulation.""" # processed sampling rate; field `sampling_rate` of the `EmulatorController` @@ -217,23 +276,21 @@ def _pulse_hamiltonian( # the oscillation and correctly solve the system evolution, hence we # set a nyquist frequency to define the timesteps in order to compute the solution times = tlist(sequence) - channels, raw_coefficients = [], [] - for operator, waveforms in hamiltonians( - sequence, configs, self.engine, self.sampling_rate - ): - raw = channel_coefficients( - waveforms, sampling_rate=self.sampling_rate, times=times + channels: list[TimeDependentOperator] = [ + TimeDependentOperator( + ( + operator, + channel_coefficients( + waveforms, sampling_rate=self.sampling_rate, times=times + ), + ) ) - channels.append(operator) - raw_coefficients.append(raw) - - return ( - OperatorEvolution( - operators=channels, coefficients=np.stack(raw_coefficients), times=times + for operator, waveforms in hamiltonians( + sequence, configs, self.engine, self.sampling_rate ) - if len(channels) > 0 - else None - ) + ] + + return OperatorEvolution(operators=channels, times=times) def update_sequence(sequence: PulseSequence, updates: dict) -> PulseSequence: @@ -256,6 +313,7 @@ def tlist(sequence: PulseSequence) -> NDArray: or Readout operation, it is excluded from the duration calculation. """ + # TODO: maybe this can be a fragility in the case of 0 duration pulses. end = max(sequence.duration, SAMPLING_INTERVAL) return np.arange(0, end, SAMPLING_INTERVAL) @@ -270,7 +328,9 @@ def hamiltonian( ) -> tuple[Operator, list[Modulated]]: n = hamiltonian.transmon_levels op = engine.expand( - config.operator(n=n, engine=engine), hamiltonian.dims, hilbert_space_index + op=config.operator(n=n, engine=engine), + targets=hilbert_space_index, + dims=hamiltonian.dims, ) waveforms = ( waveform(pulse, config, hamiltonian.qubits[hilbert_space_index], sampling_rate) @@ -306,7 +366,7 @@ def channel_coefficients( waveforms: Iterable[Modulated], sampling_rate: int, times: NDArray, -) -> BSpline: +) -> NDArray: """ Generate a B-spline interpolation of waveforms over a time evolution. This function processes a sequence of pulses, accumulating their waveforms @@ -319,11 +379,16 @@ def channel_coefficients( cumulative_phase = 0 cumulative_time = 0 for pulse in waveforms: - next_pulse_time = cumulative_time + pulse.duration - pulse_times_idx = (times >= cumulative_time) & (times < next_pulse_time) + times_ = times - cumulative_time # local times + # in this mask we take into account finite sampling rate with might mismatch with timestep of the emulator + # (i.e. float time durations and int sampling_rate) + pulse_times_idx = (times_ >= 0) & ( + times_ < int(pulse.duration * sampling_rate) / sampling_rate + ) times_samples = np.floor( (times[pulse_times_idx] - cumulative_time) * sampling_rate ).astype(int) + # in case of virtual operations (such as VirtualZ or in general # zero-duration pulses), we apply the phase jump without # affecting the waveform @@ -333,7 +398,6 @@ def channel_coefficients( ) cumulative_phase += pulse.phase - cumulative_time = next_pulse_time + cumulative_time += pulse.duration - # return pulse_waveforms return pulse_waveforms diff --git a/src/qibolab/_core/instruments/emulator/engine/abstract.py b/src/qibolab/_core/instruments/emulator/engine/abstract.py index f97fb564e..efef75140 100644 --- a/src/qibolab/_core/instruments/emulator/engine/abstract.py +++ b/src/qibolab/_core/instruments/emulator/engine/abstract.py @@ -4,10 +4,10 @@ from dataclasses import dataclass, field from typing import Protocol +import numpy as np from numpy.typing import NDArray -from scipy.interpolate import BSpline, PPoly -from ....serialize import Model +from qibolab._core.serialize import Model __all__ = [ "SimulationEngine", @@ -16,14 +16,20 @@ "OperatorEvolution", ] +HAMILTONIAN_FILENAME = "System_Hamiltonian" +SWEEP_SIMULATION_FILENAME = "Time_Coefficients_and_Results" +SIMULATOR_CONFIG = "Simulator_Configs" + +SPLINE_INTERP_ORDER = 3 +"""Polynomial order used for interpolating the pulses with a spline function.""" + +INTEGRATION_MAX_TIME_STEP = 0.02 +"""ns, min resolution of the integrator""" INTEGRATION_MULTIPLIER = 200 """factor for computing max number of steps for the ode solver""" INTEGRATION_MIN_TIME_STEP = 5e-3 """ns, max resolution of the integrator""" -HAMILTONIAN_FILENAME = "System_Hamiltonian" -STATE_FILENAME = "State_Evolution" - class Operator(Protocol): """Abstract operator interface. @@ -41,13 +47,8 @@ def __add__(self, other: "Operator") -> "Operator": """Add two operators.""" -class TimeDependentOperator(Protocol): - """Abstract time dependent operator interface.""" - - operator: Operator - """Operator.""" - time: BSpline - """Time function.""" +TimeDependentOperator = tuple[Operator, NDArray] +"""Abstract time dependent operator type.""" class EvolutionResult(Protocol): @@ -62,8 +63,9 @@ class OperatorEvolution: """Abstract operator evolution interface.""" operators: list[Operator | TimeDependentOperator] = field(default_factory=list) - coefficients: NDArray | None = None - times: NDArray | None = None + """List of static or time-dependent operators for evolution.""" + times: NDArray = field(default_factory=lambda: np.array([], dtype=float)) + """Evolution times with time step equal to the waveforms resolution.""" class SimulationEngine(Model, ABC): @@ -82,7 +84,7 @@ def evolve( time: list[float], collapse_operators: list[Operator] = None, **kwargs, - ) -> EvolutionResult: + ) -> tuple[EvolutionResult, dict]: """Evolve the system.""" @abstractmethod @@ -103,40 +105,10 @@ def tensor(self, operators: list[Operator]) -> Operator: @abstractmethod def expand( - self, op: Operator, targets: int | list[int], dims: list[int] + self, op: Operator, dims: list[int], targets: int | list[int] ) -> Operator: """Expand operator in larger Hilbert space.""" @abstractmethod def basis(self, n: int, state: int) -> Operator: """Basis operator for n levels system.""" - - @abstractmethod - def save_operators(self, operators, dump_dir) -> None: - """Persist static operators in the engine's native format.""" - - -def _spline_function(spline: BSpline): - """Convert a SciPy spline into a JAX-traceable piecewise polynomial. - - SciPy ``BSpline.__call__`` cannot be traced by JAX, so the spline is - converted once into its piecewise-polynomial representation and evaluated - with a Horner scheme on the JAX side, preserving the cubic interpolation - of the QuTiP engine exactly. - """ - import jax.numpy as jnp - - polynomial = PPoly.from_spline(spline) - breaks = jnp.asarray(polynomial.x) - coefficients = jnp.asarray(polynomial.c) - - def evaluate(t): - index = jnp.searchsorted(breaks, t, side="right") - 1 - index = jnp.clip(index, 0, breaks.size - 2) - shifted_t = t - breaks[index] - value = coefficients[0, index] - for coefficient in coefficients[1:]: - value = value * shifted_t + coefficient[index] - return value - - return evaluate diff --git a/src/qibolab/_core/instruments/emulator/engine/dynamiqs.py b/src/qibolab/_core/instruments/emulator/engine/dynamiqs.py index 6fe0c9265..007f7a35b 100644 --- a/src/qibolab/_core/instruments/emulator/engine/dynamiqs.py +++ b/src/qibolab/_core/instruments/emulator/engine/dynamiqs.py @@ -3,22 +3,19 @@ from collections.abc import Iterable from dataclasses import dataclass from functools import cached_property -from pathlib import Path from typing import Any, Literal import numpy as np from scipy.interpolate import make_interp_spline from .abstract import ( - HAMILTONIAN_FILENAME, INTEGRATION_MIN_TIME_STEP, INTEGRATION_MULTIPLIER, - STATE_FILENAME, Operator, OperatorEvolution, SimulationEngine, - _spline_function, ) +from .gpu_interpolation import jax_interpolation __all__ = ["DynamiqsEngine"] @@ -149,74 +146,6 @@ def _method(self, time: np.ndarray): rtol=self.rtol, atol=self.atol, max_steps=max(max_steps, 100_000) ) - def dump_results(self, hamiltonian: Any, sim_results: Any, dump_dir: Path) -> None: - """Save the Hamiltonian and simulation results to files with incremented naming. - - Dynamiqs time-dependent Hamiltonians hold JAX callables that cannot be - pickled, so instead of the QuTiP engine's ``qsave`` format the engine - stores plain NumPy archives: the Hamiltonian evaluated at the saved - times and the density matrices at the saved times. - """ - dump_dir.mkdir(parents=True, exist_ok=True) - - count_1 = sum( - 1 - for file in dump_dir.iterdir() - if file.is_file() and HAMILTONIAN_FILENAME in file.name - ) - count_2 = sum( - 1 - for file in dump_dir.iterdir() - if file.is_file() and STATE_FILENAME in file.name - ) - count = max(count_1, count_2) - - times = np.asarray(sim_results.tsave) - hamiltonians = np.stack([np.asarray(hamiltonian(t).to_jax()) for t in times]) - states = np.asarray(sim_results.states.to_jax()) - np.savez( - dump_dir / f"{HAMILTONIAN_FILENAME}_{count}.npz", - times=times, - hamiltonians=hamiltonians, - ) - np.savez(dump_dir / f"{STATE_FILENAME}_{count}.npz", times=times, states=states) - - def load_results( - self, dump_dir: Path, count: int | None = None - ) -> tuple[dict, dict]: - """Load the Hamiltonian and simulation results saved by :meth:`dump_results`. - - Returns two dictionaries with ``times`` and ``hamiltonians`` / ``states`` - arrays. If `count` is not given, the latest dump is loaded. - """ - dump_dir = Path(dump_dir) - if count is None: - count_1 = sum( - 1 - for file in dump_dir.iterdir() - if file.is_file() and HAMILTONIAN_FILENAME in file.name - ) - count_2 = sum( - 1 - for file in dump_dir.iterdir() - if file.is_file() and STATE_FILENAME in file.name - ) - count = max(count_1, count_2) - 1 - - hamiltonians = dict(np.load(dump_dir / f"{HAMILTONIAN_FILENAME}_{count}.npz")) - states = dict(np.load(dump_dir / f"{STATE_FILENAME}_{count}.npz")) - return hamiltonians, states - - def save_operators(self, operators, dump_dir: Path) -> None: - """Persist static operators once per experiment.""" - np.savez( - dump_dir / "operators.npz", - **{ - f"operator_{index}": np.asarray(_unwrap(operator).to_jax()) - for index, operator in enumerate(operators) - }, - ) - def evolve( self, hamiltonian: Operator, @@ -224,7 +153,6 @@ def evolve( time: Iterable[float], time_hamiltonian: OperatorEvolution = None, collapse_operators: list[Operator] = None, - save_evolution: Path | None = None, **kwargs, ) -> DynamiqsEvolutionResult: """Evolve the system.""" @@ -234,15 +162,10 @@ def evolve( hamiltonian = self.engine.constant(_unwrap(hamiltonian)) if time_hamiltonian is not None: times = time_hamiltonian.times - coefficients = [ - make_interp_spline(times, coefficient, k=SPLINE_INTERP_ORDER) - for coefficient in time_hamiltonian.coefficients - ] - for operator, coefficient in zip( - time_hamiltonian.operators, coefficients, strict=True - ): + for operator, coefficient in time_hamiltonian.operators: + spline = make_interp_spline(times, coefficient, k=SPLINE_INTERP_ORDER) hamiltonian += self.engine.modulated( - _spline_function(coefficient), _unwrap(operator) + jax_interpolation(spline), _unwrap(operator) ) method = kwargs.pop("method", self._method(time)) @@ -257,14 +180,7 @@ def evolve( **kwargs, ) - if save_evolution is not None: - self.dump_results( - hamiltonian=hamiltonian, - sim_results=result, - dump_dir=save_evolution, - ) - - return DynamiqsEvolutionResult(result) + return DynamiqsEvolutionResult(result), {"engine": "dynamiqs"} def create(self, n: int) -> Operator: """Create operator for n levels system.""" @@ -283,13 +199,13 @@ def tensor(self, operators: list[Operator]) -> Operator: return DynamiqsOperator(self.engine.tensor(*[_unwrap(op) for op in operators])) def expand( - self, op: Operator, targets: int | list[int], dims: list[int] + self, op: Operator, dims: list[int], targets: int | list[int] ) -> Operator: """Expand operator in larger Hilbert space.""" # The current emulator calls this method with QuTiP's argument order: # ``expand_operator(op, dims, targets)``. - dimensions = list(targets) - target_subsystems = [dims] if isinstance(dims, int) else list(dims) + dimensions = list(dims) + target_subsystems = [targets] if isinstance(targets, int) else list(targets) op_array = np.asarray(_unwrap(op).to_jax()) return DynamiqsOperator( self.engine.asqarray( @@ -302,6 +218,12 @@ def basis(self, dim: int | list[int], state: int | list[int]) -> Operator: """Basis operator for n levels system.""" return DynamiqsOperator(self.engine.basis(dim, state)) + def save_operators(self) -> None: + """Persist the static operators once per experiment.""" + # TODO: this function is now deprecated since the saving is upstream in emulator.py; + # however is necessary to define the load_function for loading results in dynamiqs. + return + def _unwrap(operator): return operator.qarray if isinstance(operator, DynamiqsOperator) else operator diff --git a/src/qibolab/_core/instruments/emulator/engine/gpu_interpolation.py b/src/qibolab/_core/instruments/emulator/engine/gpu_interpolation.py new file mode 100644 index 000000000..d34944c23 --- /dev/null +++ b/src/qibolab/_core/instruments/emulator/engine/gpu_interpolation.py @@ -0,0 +1,37 @@ +"""GPU interpolation utilities for efficient spline evaluation in JAX. + +This module provides tools to convert SciPy splines into JAX-traceable +piecewise polynomial functions, enabling GPU-accelerated interpolation +while preserving numerical accuracy of cubic spline interpolation. +""" + +from collections.abc import Callable, Iterable + +from numpy.typing import NDArray +from scipy.interpolate import BSpline, PPoly + + +def jax_interpolation(spline: BSpline) -> Callable[[NDArray], Iterable[float]]: + """Convert a SciPy spline into a JAX-traceable piecewise polynomial. + + SciPy ``BSpline.__call__`` cannot be traced by JAX, so the points and coefficients are + evaluated with a Horner scheme on the JAX side, preserving the cubic interpolation + of the QuTiP engine exactly. + """ + # TODO: check if there is a more efficient implementation + import jax.numpy as jnp + + polynomial = PPoly.from_spline(spline) + breaks = jnp.asarray(polynomial.x) + coefficients = jnp.asarray(polynomial.c) + + def evaluate(t): + index = jnp.searchsorted(breaks, t, side="right") - 1 + index = jnp.clip(index, 0, breaks.size - 2) + shifted_t = t - breaks[index] + value = coefficients[0, index] + for coefficient in coefficients[1:]: + value = value * shifted_t + coefficient[index] + return value + + return evaluate diff --git a/src/qibolab/_core/instruments/emulator/engine/qutip.py b/src/qibolab/_core/instruments/emulator/engine/qutip.py index a7681822a..5df9ab1d6 100644 --- a/src/qibolab/_core/instruments/emulator/engine/qutip.py +++ b/src/qibolab/_core/instruments/emulator/engine/qutip.py @@ -1,47 +1,35 @@ -from collections.abc import Iterable +import json +import os +from collections.abc import Callable, Iterable from functools import cached_property -from importlib import import_module from pathlib import Path from typing import Literal import numpy as np +from numpy.typing import NDArray from scipy.interpolate import make_interp_spline from .abstract import ( + HAMILTONIAN_FILENAME, + INTEGRATION_MAX_TIME_STEP, INTEGRATION_MIN_TIME_STEP, INTEGRATION_MULTIPLIER, + SIMULATOR_CONFIG, + SPLINE_INTERP_ORDER, + SWEEP_SIMULATION_FILENAME, Operator, OperatorEvolution, SimulationEngine, - _spline_function, + TimeDependentOperator, ) -__all__ = ["QutipEngine"] - -INTEGRATION_MAX_TIME_STEP = 0.02 -"""ns, min resolution of the integrator""" - -GPU_DTYPE_PLUGINS = {"jax": "qutip_jax", "jaxdia": "qutip_jax", "cupyd": "qutip_cupy"} -"""QuTiP data-layer dtypes usable on GPU and the plugin packages providing them.""" -JAX_GPU_DTYPES = {"jax", "jaxdia"} -"""QuTiP GPU dtypes backed by JAX.""" - -SPLINE_INTERP_ORDER = 3 +__all__ = ["QutipEngine", "load_simulation"] class QutipEngine(SimulationEngine): """Qutip simulation engine.""" device: Literal["cpu", "gpu"] = "cpu" - """Device used by QuTiP operators.""" - gpu_dtype: str = "jax" - """QuTiP data-layer dtype used for GPU arrays. - - ``jax``/``jaxdia`` use the qutip-jax plugin (recommended, available on - PyPI) together with the diffrax integrator, so the whole evolution stays - on the accelerator. ``cupyd`` uses the experimental qutip-cupy plugin - (not released on PyPI), where the ODE integration remains on the CPU. - """ @cached_property def engine(self): @@ -50,57 +38,44 @@ def engine(self): import qutip as qt if self.device == "gpu": - plugin = GPU_DTYPE_PLUGINS.get(self.gpu_dtype) - if plugin is None: - raise ValueError( - f"Unknown gpu_dtype {self.gpu_dtype!r}; " - f"supported values: {sorted(GPU_DTYPE_PLUGINS)}." - ) - try: - import_module(plugin) - except ImportError as exc: - raise ImportError( - f"QutipEngine(device='gpu', gpu_dtype={self.gpu_dtype!r}) " - f"requires the optional {plugin.replace('_', '-')} package " - "and a working CUDA-enabled backend." - ) from exc - if self.gpu_dtype in JAX_GPU_DTYPES: - self._require_jax_gpu_backend() + import jax - return qt + devices = jax.devices() + if self.device == "gpu" and any( + device.platform == "gpu" for device in devices + ): + import qutip_jax # noqa: F401 + else: + object.__setattr__(self, "device", "cpu") - def _require_jax_gpu_backend(self) -> None: - """Validate that qutip-jax is connected to an accelerator backend.""" - import jax - - devices = jax.devices() - if not any(device.platform == "gpu" for device in devices): - available = ( - ", ".join(f"{device.platform}:{device}" for device in devices) or "none" - ) - raise ImportError( - f"QutipEngine(device='gpu', gpu_dtype={self.gpu_dtype!r}) requires " - "a CUDA-enabled JAX backend; available JAX devices: " - f"{available}. Install a CUDA-enabled JAX runtime, for example " - "`jax[cuda12]`, or use device='cpu'." - ) + return qt def _to_device(self, op: Operator) -> Operator: """Move a QuTiP operator to the selected data layer.""" - return op.to(self.gpu_dtype) if self.device == "gpu" else op + return op.to("jax") if self.device == "gpu" else op - def save_operators(self, operators, dump_dir: Path) -> None: - """Persist the static operators once per experiment.""" - self.engine.qsave(operators, str(dump_dir / "operators")) + def interpolate_coeffs( + self, x: NDArray, y: NDArray + ) -> Callable[[NDArray], Iterable[float]]: + + spline = make_interp_spline(x, y, k=SPLINE_INTERP_ORDER) + if self.device == "gpu": + import jax + + from .gpu_interpolation import jax_interpolation + + # TODO: check if there is a more efficient implementation for jax_interpolation + return jax.jit(jax_interpolation(spline)) + else: + return spline def evolve( self, hamiltonian: Operator, initial_state: Operator, time: Iterable[float], - time_hamiltonian: OperatorEvolution = None, - collapse_operators: list[Operator] = None, - save_evolution: Path | None = None, + time_hamiltonian: OperatorEvolution, + collapse_operators: list[Operator] | None = None, **kwargs, ): """Evolve the system.""" @@ -110,50 +85,36 @@ def evolve( # below calls ``op.to(self.gpu_dtype)``, which only works once the # qutip-jax/qutip-cupy plugin has registered that dtype; this also # validates ``gpu_dtype`` early instead of part-way through the evolution - _ = self.engine time_diff = np.diff(time) nsteps = max(time_diff) / INTEGRATION_MIN_TIME_STEP * INTEGRATION_MULTIPLIER - # not every SciPy solvers accepts as parameters min_step, that's why we - # define nsteps instead - options = {"max_step": INTEGRATION_MAX_TIME_STEP, "nsteps": nsteps} - if self.device == "gpu" and self.gpu_dtype in ("jax", "jaxdia"): + + if self.device == "gpu": + import diffrax # the default scipy integrators would transfer the state between # device and host on every step; the diffrax integrator provided # by qutip-jax keeps the whole evolution on the accelerator, with # the same maximum-step bound enforced through its controller - from diffrax import PIDController options = { "method": "diffrax", - "stepsize_controller": PIDController( + "stepsize_controller": diffrax.PIDController( rtol=1e-6, atol=1e-8, dtmax=INTEGRATION_MAX_TIME_STEP ), "max_steps": int(nsteps), } - if time_hamiltonian is not None: - times = time_hamiltonian.times - coefficients = [ - make_interp_spline(times, coefficient, k=SPLINE_INTERP_ORDER) - for coefficient in time_hamiltonian.coefficients - ] - if "method" in options: - # diffrax jits over the coefficients, and the spline objects - # are not traceable by JAX; convert them with the shared spline - # helper so the coefficients become jittable functions - from jax import jit - - coefficients = [ - jit(_spline_function(coefficient)) for coefficient in coefficients - ] - hamiltonian = [self._to_device(hamiltonian)] + [ - [self._to_device(operator), coefficient] - for operator, coefficient in zip( - time_hamiltonian.operators, coefficients, strict=True - ) + else: # only cpu available, using SciPy solver + # not every SciPy solvers accepts as parameters min_step, that's why we + # define nsteps instead + options = {"max_step": INTEGRATION_MAX_TIME_STEP, "nsteps": nsteps} + + hamiltonian = [self._to_device(hamiltonian)] + [ + [ + self._to_device(operator), + self.interpolate_coeffs(time_hamiltonian.times, coefficient), ] - else: - hamiltonian = self._to_device(hamiltonian) + for operator, coefficient in time_hamiltonian.operators + ] sim_results = self.engine.mesolve( hamiltonian, @@ -164,7 +125,7 @@ def evolve( **kwargs, ) - return sim_results + return sim_results, options def create(self, n: int) -> Operator: """Create operator for n levels system.""" @@ -182,10 +143,55 @@ def tensor(self, operators: list[Operator]) -> Operator: """Tensor product of a list of operators.""" return self._to_device(self.engine.tensor(*operators)) - def expand(self, op: Operator, targets: int | list[int], dims: list[int]): + def expand(self, op: Operator, dims: list[int], targets: int | list[int]): """Expand operator in larger Hilbert space.""" - return self._to_device(self.engine.expand_operator(op, targets, dims)) + return self._to_device(self.engine.expand_operator(op, dims, targets)) def basis(self, dim: int, state: int) -> Operator: """Basis operator for n levels system.""" return self._to_device(self.engine.basis(dimensions=dim, n=state)) + + +def load_simulation( + simulation_path: str | os.PathLike, sequence_index: int, sweep_index: int +) -> tuple[OperatorEvolution, np.typing.NDArray, dict]: + """Load simulation results from disk. + + Args: + simulation_path: Path to the simulation directory. + sequence_index: Index of the sequence to load. + sweep_index: Index of the sweep to load. + + Returns: + A tuple containing: + - system: List with Hamiltonian and time-dependent operators. + - result_states: Array of simulated result states. + - sim_configs: Dictionary of simulation configuration parameters. + + Raises: + NotADirectoryError: If the sequence directory does not exist. + """ + simulation_path = Path(simulation_path) + + simulated_sequence_path = simulation_path / f"sequence_{sequence_index}" + if not simulated_sequence_path.is_dir(): + raise NotADirectoryError + + hamiltonians = np.load(simulated_sequence_path / (HAMILTONIAN_FILENAME + ".npy")) + sweep_sim = np.load( + simulated_sequence_path / (SWEEP_SIMULATION_FILENAME + f"_{sweep_index}.npz") + ) + result_states = sweep_sim["results"] + time_coeffs = sweep_sim["time_coeffs"] + + with open(simulated_sequence_path / (SIMULATOR_CONFIG + ".json")) as f: + sim_configs = json.load(f) + + system = [Operator(hamiltonians[0])] + [ + TimeDependentOperator([ham, coeffs]) + for ham, coeffs in zip(hamiltonians[1:], time_coeffs[1:]) + ] + timesteps = time_coeffs[0] + system_evo = OperatorEvolution(operators=system, times=timesteps) + + return system_evo, result_states, sim_configs diff --git a/src/qibolab/_core/instruments/emulator/hamiltonians.py b/src/qibolab/_core/instruments/emulator/hamiltonians.py index b9c6e2fa2..a3ee24fce 100644 --- a/src/qibolab/_core/instruments/emulator/hamiltonians.py +++ b/src/qibolab/_core/instruments/emulator/hamiltonians.py @@ -44,7 +44,7 @@ class FluxEmulatorConfig(Config): @staticmethod def operator(n: int, engine: SimulationEngine) -> Operator: - return engine.create(n) * engine.destroy(n) + return engine.create(n) @ engine.destroy(n) @property def flux(self) -> float: @@ -81,15 +81,19 @@ def detuned_frequency(self, flux: float) -> float: def operator(self, n: int, engine: SimulationEngine, flux: float = 0): """Time independent operator.""" - quadratic_term = engine.create(n) * engine.destroy(n) * self.omega(flux) / giga + quadratic_term = ( + (engine.create(n) @ engine.destroy(n)) * self.omega(flux) / giga + ) quartic_term = ( self.anharmonicity * np.pi / giga - * engine.create(n) - * engine.create(n) - * engine.destroy(n) - * engine.destroy(n) + * ( + engine.create(n) + @ engine.create(n) + @ engine.destroy(n) + @ engine.destroy(n) + ) ) return quadratic_term + quartic_term @@ -100,8 +104,10 @@ def t_phi(self, transition: TransitionId) -> float: def relaxation(self, n: int, engine: SimulationEngine) -> Operator: return sum( np.sqrt(1 / t1) - * engine.basis(state=transition[0], dim=n) - * engine.basis(state=transition[1], dim=n).dag() + * ( + engine.basis(state=transition[0], dim=n) + @ engine.basis(state=transition[1], dim=n).dag() + ) for transition, t1 in self.t1.items() ) @@ -110,9 +116,9 @@ def dephasing(self, n: int, engine: SimulationEngine) -> Operator: np.sqrt(1 / self.t_phi(pair) / 2) * ( engine.basis(state=pair[0], dim=n) - * engine.basis(state=pair[0], dim=n).dag() + @ engine.basis(state=pair[0], dim=n).dag() - engine.basis(state=pair[1], dim=n) - * engine.basis(state=pair[1], dim=n).dag() + @ engine.basis(state=pair[1], dim=n).dag() ) for pair in self.t2 ) @@ -293,21 +299,21 @@ def hamiltonian(self, config: dict, engine: SimulationEngine) -> Operator: """Time independent part of Hamiltonian.""" qubit_terms = sum( engine.expand( - qubit.operator( + op=qubit.operator( n=self.transmon_levels, flux=static_flux(qubit=i, config=config), engine=engine, ), - self.dims, - self.hilbert_space_index(i), + dims=self.dims, + targets=self.hilbert_space_index(i), ) for i, qubit in self.qubits.items() ) coupling = sum( engine.expand( - pair.operator(self.transmon_levels, engine), - self.dims, - [ + op=pair.operator(self.transmon_levels, engine), + dims=self.dims, + targets=[ self.hilbert_space_index(pair_id[0]), self.hilbert_space_index(pair_id[1]), ], @@ -325,17 +331,17 @@ def dissipation(self, engine: SimulationEngine) -> Operator: if len(qubit.t1) > 0: collapse_operators.append( engine.expand( - qubit.relaxation(self.transmon_levels, engine), - self.dims, - self.hilbert_space_index(i), + op=qubit.relaxation(self.transmon_levels, engine), + dims=self.dims, + targets=self.hilbert_space_index(i), ) ) if len(qubit.t2) > 0: collapse_operators.append( engine.expand( - qubit.dephasing(self.transmon_levels, engine), - self.dims, - self.hilbert_space_index(i), + op=qubit.dephasing(self.transmon_levels, engine), + dims=self.dims, + targets=self.hilbert_space_index(i), ) ) return collapse_operators diff --git a/src/qibolab/_core/instruments/emulator/results.py b/src/qibolab/_core/instruments/emulator/results.py index 4220e3ff8..d55e22602 100644 --- a/src/qibolab/_core/instruments/emulator/results.py +++ b/src/qibolab/_core/instruments/emulator/results.py @@ -16,14 +16,15 @@ import numpy as np from numpy.typing import NDArray +from qibolab._core.identifier import ChannelId, Result +from qibolab._core.pulses import Acquisition, Align, PulseId, Readout +from qibolab._core.sequence import PulseSequence + from ...execution_parameters import ( AcquisitionType, AveragingMode, ExecutionParameters, ) -from ...identifier import ChannelId, Result -from ...pulses import Acquisition, Align, PulseId, Readout -from ...sequence import PulseSequence from .engine import Operator from .hamiltonians import HamiltonianConfig @@ -167,7 +168,7 @@ def _cyclic_results( sequence: PulseSequence, hamiltonian: HamiltonianConfig, options: ExecutionParameters, -) -> dict[int, Result]: +) -> dict[PulseId, Result]: """Process measurement results from a cyclic quantum simulation, where the output for each measurement is excited state population. Computes readout results by projecting quantum state probabilities onto @@ -197,7 +198,7 @@ def _singleshot_results( sequence: PulseSequence, hamiltonian: HamiltonianConfig, options: ExecutionParameters, -) -> dict[int, Result]: +) -> dict[PulseId, Result]: """Extract measurement results from simulated quantum state probabilities. Performs single-shot measurement extraction from state probabilities by sampling according to the specified number of shots and mapping readout operations to their diff --git a/tests/instruments/emulator/platforms/fixed-frequency-qutrits/platform.py b/tests/instruments/emulator/platforms/fixed-frequency-qutrits/platform.py index ab785dd29..ca827fa7e 100644 --- a/tests/instruments/emulator/platforms/fixed-frequency-qutrits/platform.py +++ b/tests/instruments/emulator/platforms/fixed-frequency-qutrits/platform.py @@ -44,7 +44,7 @@ def create() -> Hardware: address="0.0.0.0", channels=channels, sampling_rate_=2, - save_dir=None, + # save_dir="./emulator_dumping_test_1", ), } diff --git a/tests/instruments/emulator/test_dynamiqs.py b/tests/instruments/emulator/test_dynamiqs.py index f8e53b76d..2bc836499 100644 --- a/tests/instruments/emulator/test_dynamiqs.py +++ b/tests/instruments/emulator/test_dynamiqs.py @@ -10,11 +10,12 @@ from qibolab._core.instruments.emulator.emulator import EmulatorController from qibolab._core.instruments.emulator.engine import DynamiqsEngine, QutipEngine from qibolab._core.instruments.emulator.engine.abstract import OperatorEvolution +from qibolab._core.instruments.emulator.engine.gpu_interpolation import ( + jax_interpolation, +) pytest.importorskip("dynamiqs") -from qibolab._core.instruments.emulator.engine.abstract import _spline_function - PLATFORMS_DIR = Path(__file__).parent / "platforms" PLATFORM_NAMES = sorted(p.name for p in PLATFORMS_DIR.iterdir() if p.is_dir()) @@ -70,12 +71,12 @@ def test_dynamiqs_two_body_expand_matches_qutip(): ) -def test_spline_function_matches_scipy(): +def test_jax_interpolation_matches_scipy(): """The JAX-traceable conversion reproduces the cubic spline exactly.""" times = np.linspace(0.0, 20.0, 101) values = np.sin(np.pi * times / 20.0) ** 2 * np.cos(2 * np.pi * 0.3 * times) spline = make_interp_spline(times, values, k=3) - evaluate = _spline_function(spline) + evaluate = jax_interpolation(spline) dense = np.linspace(0.0, 20.0, 1009) converted = np.array([float(evaluate(t)) for t in dense]) @@ -87,16 +88,17 @@ def test_dynamiqs_evolution_matches_qutip(): qutip = QutipEngine() times = np.linspace(0, 1, 5) - dynamiqs_result = dynamiqs.evolve( + dynamiqs_result, _ = dynamiqs.evolve( hamiltonian=dynamiqs.identity(2) * 0, initial_state=dynamiqs.basis(2, 1), time=times, collapse_operators=[0.2 * dynamiqs.destroy(2)], ) - qutip_result = qutip.evolve( + qutip_result, _ = qutip.evolve( hamiltonian=qutip.identity(2) * 0, initial_state=qutip.basis(2, 1), time=times, + time_hamiltonian=OperatorEvolution(), collapse_operators=[0.2 * qutip.destroy(2)], ) @@ -123,11 +125,10 @@ def _driven_evolution(engine, **kwargs): * np.cos(2 * np.pi * 0.2 * times) ) evolution = OperatorEvolution( - operators=[a + a.dag()], - coefficients=np.stack([coefficient]), + operators=[(a + a.dag(), coefficient)], times=times, ) - result = engine.evolve( + result, _ = engine.evolve( hamiltonian=hamiltonian, initial_state=engine.basis(3, 0), time=np.linspace(0.0, 20.0, 5), @@ -154,6 +155,9 @@ def test_dynamiqs_driven_evolution_matches_qutip(engine): np.testing.assert_allclose(states, reference, atol=1e-4) +@pytest.mark.skip( + reason="now dumping and loading is different, has to be reimplemented for dynamiqs" +) def test_dynamiqs_dump_and_load_results(tmp_path): engine = DynamiqsEngine() states = _driven_evolution(engine, save_evolution=tmp_path) diff --git a/tests/instruments/emulator/test_gpu.py b/tests/instruments/emulator/test_gpu.py index 90fc537c6..5a80831c1 100644 --- a/tests/instruments/emulator/test_gpu.py +++ b/tests/instruments/emulator/test_gpu.py @@ -1,10 +1,5 @@ """Testing emulator GPU configuration.""" -import importlib.util -import os -import subprocess -import sys - import numpy as np import pytest @@ -24,55 +19,13 @@ def test_qutip_cpu_device_is_default(): assert np.allclose(engine.destroy(3).full(), QutipEngine().destroy(3).full()) -def test_qutip_gpu_unknown_dtype(): - with pytest.raises(ValueError, match="gpu_dtype"): - QutipEngine(device="gpu", gpu_dtype="unknown").destroy(2) - - -@pytest.mark.parametrize( - "gpu_dtype,plugin", [("jax", "qutip_jax"), ("cupyd", "qutip_cupy")] -) -def test_qutip_gpu_requires_plugin(gpu_dtype, plugin): - if importlib.util.find_spec(plugin) is not None: - pytest.skip(f"{plugin} is installed in this environment.") - - with pytest.raises(ImportError, match=plugin.replace("_", "-")): - QutipEngine(device="gpu", gpu_dtype=gpu_dtype).destroy(2) - - -def test_qutip_gpu_jax_requires_jax_gpu_backend(): - if importlib.util.find_spec("qutip_jax") is None: - pytest.skip("qutip_jax is not installed in this environment.") - - env = os.environ.copy() - env["JAX_PLATFORMS"] = "cpu" - result = subprocess.run( - [ - sys.executable, - "-c", - ( - "from qibolab._core.instruments.emulator.engine import QutipEngine; " - "QutipEngine(device='gpu', gpu_dtype='jax').destroy(2)" - ), - ], - env=env, - capture_output=True, - text=True, - check=False, - ) - - assert result.returncode != 0 - assert "CUDA-enabled JAX backend" in result.stderr - - -@pytest.mark.parametrize("gpu_dtype", ["jax", "cupyd"]) +@pytest.mark.parametrize("gpu_dtype", ["jax"]) def test_qutip_gpu_data_layer(gpu_dtype): - plugin = "qutip_jax" if gpu_dtype == "jax" else "qutip_cupy" - pytest.importorskip(plugin) + pytest.importorskip(gpu_dtype) if gpu_dtype == "jax" and not _has_jax_gpu(): pytest.skip("CUDA-enabled JAX backend is not available.") - engine = QutipEngine(device="gpu", gpu_dtype=gpu_dtype) + engine = QutipEngine(device="gpu") operator = engine.create(3) * engine.destroy(3) assert type(operator.data).__name__ != "Dense" assert np.allclose(operator.full(), np.diag([0.0, 1.0, 2.0]))